Tidy summarizes information about the components of a model.
A model component might be a single term in a regression, a single
hypothesis, a cluster, or a class. Exactly what tidy considers to be a
model component varies cross models but is usually self-evident.
If a model has several distinct types of components, you will need to
specify which components to return.

# S3 method for geeglm
tidy(x, conf.int = FALSE, conf.level = 0.95,
exponentiate = FALSE, quick = FALSE, ...)

## Arguments

x |
A `geeglm` object returned from a call to `geepack::geeglm()` . |

conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to `FALSE` . |

conf.level |
The confidence level to use for the confidence interval
if `conf.int = TRUE` . Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval. |

exponentiate |
Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to `FALSE` . |

quick |
Logical indiciating if the only the `term` and `estimate`
columns should be returned. Often useful to avoid time consuming
covariance and standard error calculations. Defaults to `FALSE` . |

... |
Additional arguments. Not used. Needed to match generic
signature only. **Cautionary note:** Misspelled arguments will be
absorbed in `...` , where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass `conf.lvel = 0.9` , all computation will
proceed using `conf.level = 0.95` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Details

If `conf.int = TRUE`

, the confidence interval is computed with
the an internal `confint.geeglm()`

function.

If you have missing values in your model data, you may need to
refit the model with `na.action = na.exclude`

or deal with the
missingness in the data beforehand.

## See also

## Value

A `tibble::tibble()`

with columns:

regresionTRUE

## Examples

#> # A tibble: 3 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4406. 407. 117. 0
#> 2 Frost 1.69 2.25 0.562 0.453
#> 3 Murder -22.7 31.4 0.522 0.470

#> # A tibble: 3 x 2
#> term estimate
#> <chr> <dbl>
#> 1 (Intercept) 4406.
#> 2 Frost 1.69
#> 3 Murder -22.7

#> # A tibble: 3 x 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 4406. 407. 117. 0 3608. 5205.
#> 2 Frost 1.69 2.25 0.562 0.453 -2.72 6.10
#> 3 Murder -22.7 31.4 0.522 0.470 -84.2 38.8